Overview

Dataset statistics

Number of variables33
Number of observations20160
Missing cells0
Missing cells (%)0.0%
Duplicate rows7
Duplicate rows (%)< 0.1%
Total size in memory5.1 MiB
Average record size in memory264.0 B

Variable types

Categorical21
Numeric12

Alerts

Dataset has 7 (< 0.1%) duplicate rowsDuplicates
Count of household members is highly correlated with Count of adult household membersHigh correlation
Household in urban/rural area is highly correlated with population densityHigh correlation
population density is highly correlated with Household in urban/rural areaHigh correlation
Price of Gasoline Affects Travel is highly correlated with Travel is a Financial BurdenHigh correlation
Travel is a Financial Burden is highly correlated with Price of Gasoline Affects TravelHigh correlation
Count of household vehicles is highly correlated with Count of adult household membersHigh correlation
Count of adult household members is highly correlated with Count of household members and 1 other fieldsHigh correlation
Race White is highly correlated with Race African and 1 other fieldsHigh correlation
Race African is highly correlated with Race WhiteHigh correlation
Race Asian is highly correlated with Race WhiteHigh correlation
Count of household members is highly correlated with Count of adult household membersHigh correlation
Household in urban/rural area is highly correlated with population densityHigh correlation
population density is highly correlated with Household in urban/rural areaHigh correlation
Price of Gasoline Affects Travel is highly correlated with Travel is a Financial BurdenHigh correlation
Travel is a Financial Burden is highly correlated with Price of Gasoline Affects TravelHigh correlation
Count of household vehicles is highly correlated with Count of adult household membersHigh correlation
Count of adult household members is highly correlated with Count of household members and 1 other fieldsHigh correlation
Race White is highly correlated with Race African and 1 other fieldsHigh correlation
Race African is highly correlated with Race WhiteHigh correlation
Race Asian is highly correlated with Race WhiteHigh correlation
Count of household members is highly correlated with Count of adult household membersHigh correlation
Household in urban/rural area is highly correlated with population densityHigh correlation
population density is highly correlated with Household in urban/rural areaHigh correlation
Count of household vehicles is highly correlated with Count of adult household membersHigh correlation
Count of adult household members is highly correlated with Count of household members and 1 other fieldsHigh correlation
Race White is highly correlated with Race African and 1 other fieldsHigh correlation
Race African is highly correlated with Race WhiteHigh correlation
Race Asian is highly correlated with Race WhiteHigh correlation
Race White is highly correlated with Race African and 1 other fieldsHigh correlation
Race African is highly correlated with Race WhiteHigh correlation
Race Asian is highly correlated with Race WhiteHigh correlation
Count of household members is highly correlated with Count of adult household membersHigh correlation
Household in urban/rural area is highly correlated with population densityHigh correlation
population density is highly correlated with Household in urban/rural areaHigh correlation
Price of Gasoline Affects Travel is highly correlated with Travel is a Financial BurdenHigh correlation
Travel is a Financial Burden is highly correlated with Price of Gasoline Affects TravelHigh correlation
Relationship is highly correlated with Count of adult household membersHigh correlation
Count of adult household members is highly correlated with Count of household members and 1 other fieldsHigh correlation
Public Transportation or Taxi is highly correlated with Bicycle or WalkHigh correlation
Bicycle or Walk is highly correlated with Public Transportation or TaxiHigh correlation
Race White is highly correlated with Race African and 2 other fieldsHigh correlation
Race African is highly correlated with Race WhiteHigh correlation
Race Asian is highly correlated with Race WhiteHigh correlation
Multiple Response Race is highly correlated with Race WhiteHigh correlation
Count of Car Share Program Usage is highly skewed (γ1 = 26.2043882) Skewed
Count of Car Share Program Usage has 20060 (99.5%) zeros Zeros
Count of Walk Trips has 4878 (24.2%) zeros Zeros
Count of Bike Trips has 18475 (91.6%) zeros Zeros

Reproduction

Analysis started2022-04-13 08:07:10.699460
Analysis finished2022-04-13 08:07:48.111095
Duration37.41 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

AFV
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
0
19277 
1
 
883

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019277
95.6%
1883
 
4.4%

Length

2022-04-13T10:07:48.225808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:48.313807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
019277
95.6%
1883
 
4.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Household income
Real number (ℝ≥0)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.582291667
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.6 KiB
2022-04-13T10:07:48.412118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile11
Maximum11
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.217964146
Coefficient of variation (CV)0.2925189697
Kurtosis-0.3729397233
Mean7.582291667
Median Absolute Deviation (MAD)2
Skewness-0.242274076
Sum152859
Variance4.919364955
MonotonicityNot monotonic
2022-04-13T10:07:48.510626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
63388
16.8%
73369
16.7%
83276
16.2%
112505
12.4%
102292
11.4%
92090
10.4%
51687
8.4%
4813
 
4.0%
3438
 
2.2%
2152
 
0.8%
ValueCountFrequency (%)
1150
 
0.7%
2152
 
0.8%
3438
 
2.2%
4813
 
4.0%
51687
8.4%
63388
16.8%
73369
16.7%
83276
16.2%
92090
10.4%
102292
11.4%
ValueCountFrequency (%)
112505
12.4%
102292
11.4%
92090
10.4%
83276
16.2%
73369
16.7%
63388
16.8%
51687
8.4%
4813
 
4.0%
3438
 
2.2%
2152
 
0.8%

Home Ownership
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
1
16353 
2
3807 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
116353
81.1%
23807
 
18.9%

Length

2022-04-13T10:07:48.620542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:48.703431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
116353
81.1%
23807
 
18.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Count of household members
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.51547619
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.6 KiB
2022-04-13T10:07:48.780256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.224191304
Coefficient of variation (CV)0.4866638407
Kurtosis1.671535091
Mean2.51547619
Median Absolute Deviation (MAD)1
Skewness1.039552633
Sum50712
Variance1.498644349
MonotonicityNot monotonic
2022-04-13T10:07:48.901073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
28514
42.2%
13718
18.4%
33585
17.8%
43039
 
15.1%
5956
 
4.7%
6238
 
1.2%
765
 
0.3%
826
 
0.1%
910
 
< 0.1%
106
 
< 0.1%
Other values (2)3
 
< 0.1%
ValueCountFrequency (%)
13718
18.4%
28514
42.2%
33585
17.8%
43039
 
15.1%
5956
 
4.7%
6238
 
1.2%
765
 
0.3%
826
 
0.1%
910
 
< 0.1%
106
 
< 0.1%
ValueCountFrequency (%)
121
 
< 0.1%
112
 
< 0.1%
106
 
< 0.1%
910
 
< 0.1%
826
 
0.1%
765
 
0.3%
6238
 
1.2%
5956
 
4.7%
43039
15.1%
33585
17.8%

Household in urban/rural area
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
1
15855 
2
4305 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
115855
78.6%
24305
 
21.4%

Length

2022-04-13T10:07:49.027216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:49.102398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
115855
78.6%
24305
 
21.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

population density
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.134176587
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.6 KiB
2022-04-13T10:07:49.168417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.883401805
Coefficient of variation (CV)0.455568785
Kurtosis-1.106189972
Mean4.134176587
Median Absolute Deviation (MAD)2
Skewness-0.2071199744
Sum83345
Variance3.547202358
MonotonicityNot monotonic
2022-04-13T10:07:49.268289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
64618
22.9%
54025
20.0%
23256
16.2%
42797
13.9%
12223
11.0%
31843
 
9.1%
71124
 
5.6%
8274
 
1.4%
ValueCountFrequency (%)
12223
11.0%
23256
16.2%
31843
 
9.1%
42797
13.9%
54025
20.0%
64618
22.9%
71124
 
5.6%
8274
 
1.4%
ValueCountFrequency (%)
8274
 
1.4%
71124
 
5.6%
64618
22.9%
54025
20.0%
42797
13.9%
31843
 
9.1%
23256
16.2%
12223
11.0%

Price of Gasoline Affects Travel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
2
5572 
4
5178 
3
3708 
5
2897 
1
2805 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row3
4th row5
5th row2

Common Values

ValueCountFrequency (%)
25572
27.6%
45178
25.7%
33708
18.4%
52897
14.4%
12805
13.9%

Length

2022-04-13T10:07:49.401867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:49.501747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
25572
27.6%
45178
25.7%
33708
18.4%
52897
14.4%
12805
13.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Travel is a Financial Burden
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
3
7087 
2
5219 
4
4884 
5
1597 
1
1373 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
37087
35.2%
25219
25.9%
44884
24.2%
51597
 
7.9%
11373
 
6.8%

Length

2022-04-13T10:07:49.611114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:49.710627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
37087
35.2%
25219
25.9%
44884
24.2%
51597
 
7.9%
11373
 
6.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Age
Real number (ℝ≥0)

Distinct72
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.49632937
Minimum18
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.6 KiB
2022-04-13T10:07:49.858314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q137
median49
Q358
95-th percentile68
Maximum92
Range74
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.29663575
Coefficient of variation (CV)0.2799508073
Kurtosis-0.800121088
Mean47.49632937
Median Absolute Deviation (MAD)10
Skewness-0.08357753603
Sum957526
Variance176.8005223
MonotonicityNot monotonic
2022-04-13T10:07:50.027540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53590
 
2.9%
58590
 
2.9%
52589
 
2.9%
56584
 
2.9%
55571
 
2.8%
54566
 
2.8%
60557
 
2.8%
59557
 
2.8%
50553
 
2.7%
57553
 
2.7%
Other values (62)14450
71.7%
ValueCountFrequency (%)
1827
 
0.1%
1959
 
0.3%
2071
 
0.4%
2179
 
0.4%
22114
 
0.6%
23178
0.9%
24220
1.1%
25235
1.2%
26283
1.4%
27309
1.5%
ValueCountFrequency (%)
921
 
< 0.1%
882
 
< 0.1%
873
 
< 0.1%
863
 
< 0.1%
856
< 0.1%
847
< 0.1%
836
< 0.1%
828
< 0.1%
814
 
< 0.1%
8014
0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
1
10632 
0
9528 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
110632
52.7%
09528
47.3%

Length

2022-04-13T10:07:50.209243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:50.307918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
110632
52.7%
09528
47.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
4
6315 
5
5839 
3
5592 
2
2288 
1
 
126

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row1
3rd row4
4th row3
5th row5

Common Values

ValueCountFrequency (%)
46315
31.3%
55839
29.0%
35592
27.7%
22288
 
11.3%
1126
 
0.6%

Length

2022-04-13T10:07:50.418106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:50.511057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
46315
31.3%
55839
29.0%
35592
27.7%
22288
 
11.3%
1126
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
2
18396 
1
 
1764

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
218396
91.2%
11764
 
8.8%

Length

2022-04-13T10:07:50.634908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:51.068567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
218396
91.2%
11764
 
8.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Job Category
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
4
12630 
1
3399 
2
2363 
3
1768 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row3
5th row2

Common Values

ValueCountFrequency (%)
412630
62.6%
13399
 
16.9%
22363
 
11.7%
31768
 
8.8%

Length

2022-04-13T10:07:51.131091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:51.201350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
412630
62.6%
13399
 
16.9%
22363
 
11.7%
31768
 
8.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Count of Car Share Program Usage
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.009672619048
Minimum0
Maximum8
Zeros20060
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size157.6 KiB
2022-04-13T10:07:51.286028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1749637791
Coefficient of variation (CV)18.08856301
Kurtosis837.2767446
Mean0.009672619048
Median Absolute Deviation (MAD)0
Skewness26.2043882
Sum195
Variance0.03061232399
MonotonicityNot monotonic
2022-04-13T10:07:51.392680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
020060
99.5%
159
 
0.3%
219
 
0.1%
38
 
< 0.1%
56
 
< 0.1%
44
 
< 0.1%
72
 
< 0.1%
61
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
020060
99.5%
159
 
0.3%
219
 
0.1%
38
 
< 0.1%
44
 
< 0.1%
56
 
< 0.1%
61
 
< 0.1%
72
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
72
 
< 0.1%
61
 
< 0.1%
56
 
< 0.1%
44
 
< 0.1%
38
 
< 0.1%
219
 
0.1%
159
 
0.3%
020060
99.5%

Trip Time to Work in Minutes
Real number (ℝ≥0)

Distinct124
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.70114087
Minimum1
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.6 KiB
2022-04-13T10:07:51.537979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q114
median20
Q335
95-th percentile62
Maximum600
Range599
Interquartile range (IQR)21

Descriptive statistics

Standard deviation27.80729965
Coefficient of variation (CV)1.003832289
Kurtosis105.4183237
Mean27.70114087
Median Absolute Deviation (MAD)10
Skewness7.261943772
Sum558455
Variance773.2459137
MonotonicityNot monotonic
2022-04-13T10:07:51.711415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152578
12.8%
202486
12.3%
301990
9.9%
101865
 
9.3%
251637
 
8.1%
451232
 
6.1%
5991
 
4.9%
40867
 
4.3%
35863
 
4.3%
60704
 
3.5%
Other values (114)4947
24.5%
ValueCountFrequency (%)
161
 
0.3%
2172
 
0.9%
3165
 
0.8%
4111
 
0.6%
5991
4.9%
6168
 
0.8%
7369
 
1.8%
8362
 
1.8%
970
 
0.3%
101865
9.3%
ValueCountFrequency (%)
6008
< 0.1%
4802
 
< 0.1%
4201
 
< 0.1%
4003
 
< 0.1%
3981
 
< 0.1%
3901
 
< 0.1%
3604
 
< 0.1%
3451
 
< 0.1%
3302
 
< 0.1%
30012
0.1%

Best estimate of annual miles
Real number (ℝ≥0)

Distinct19513
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14651.56324
Minimum0
Maximum200000
Zeros25
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size157.6 KiB
2022-04-13T10:07:51.860110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2600
Q17701.135022
median12082.68652
Q317514.79698
95-th percentile30639.43257
Maximum200000
Range200000
Interquartile range (IQR)9813.661959

Descriptive statistics

Standard deviation15697.5426
Coefficient of variation (CV)1.071390291
Kurtosis73.56609738
Mean14651.56324
Median Absolute Deviation (MAD)4799.886649
Skewness7.256563054
Sum295375514.8
Variance246412843.8
MonotonicityNot monotonic
2022-04-13T10:07:52.013056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000061
 
0.3%
1200028
 
0.1%
400026
 
0.1%
100025
 
0.1%
025
 
0.1%
300019
 
0.1%
600019
 
0.1%
200018
 
0.1%
500016
 
0.1%
1500015
 
0.1%
Other values (19503)19908
98.8%
ValueCountFrequency (%)
025
0.1%
0.9738257251
 
< 0.1%
1.207301331
 
< 0.1%
61
 
< 0.1%
8.0240280771
 
< 0.1%
104
 
< 0.1%
15.310524841
 
< 0.1%
204
 
< 0.1%
241
 
< 0.1%
31.42139281
 
< 0.1%
ValueCountFrequency (%)
20000061
0.3%
197229.74991
 
< 0.1%
1920001
 
< 0.1%
185931.47451
 
< 0.1%
1855081
 
< 0.1%
184546.1181
 
< 0.1%
1800001
 
< 0.1%
1680002
 
< 0.1%
167324.29621
 
< 0.1%
166135.78161
 
< 0.1%

Count of household vehicles
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.459623016
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.6 KiB
2022-04-13T10:07:52.142235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.239012589
Coefficient of variation (CV)0.5037408503
Kurtosis5.688584573
Mean2.459623016
Median Absolute Deviation (MAD)1
Skewness1.697817966
Sum49586
Variance1.535152197
MonotonicityNot monotonic
2022-04-13T10:07:52.242343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
28787
43.6%
34508
22.4%
13704
18.4%
41956
 
9.7%
5712
 
3.5%
6287
 
1.4%
791
 
0.5%
849
 
0.2%
937
 
0.2%
1013
 
0.1%
Other values (2)16
 
0.1%
ValueCountFrequency (%)
13704
18.4%
28787
43.6%
34508
22.4%
41956
 
9.7%
5712
 
3.5%
6287
 
1.4%
791
 
0.5%
849
 
0.2%
937
 
0.2%
1013
 
0.1%
ValueCountFrequency (%)
1210
 
< 0.1%
116
 
< 0.1%
1013
 
0.1%
937
 
0.2%
849
 
0.2%
791
 
0.5%
6287
 
1.4%
5712
 
3.5%
41956
9.7%
34508
22.4%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
0
18162 
1
 
1481
2
 
480
3
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
018162
90.1%
11481
 
7.3%
2480
 
2.4%
337
 
0.2%

Length

2022-04-13T10:07:52.352350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:52.432110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
018162
90.1%
11481
 
7.3%
2480
 
2.4%
337
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Relationship
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.410763889
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.6 KiB
2022-04-13T10:07:52.515402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8062292892
Coefficient of variation (CV)0.5714842119
Kurtosis18.93993807
Mean1.410763889
Median Absolute Deviation (MAD)0
Skewness3.628440526
Sum28441
Variance0.6500056668
MonotonicityNot monotonic
2022-04-13T10:07:52.622214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
113914
69.0%
25174
 
25.7%
3733
 
3.6%
7139
 
0.7%
673
 
0.4%
466
 
0.3%
561
 
0.3%
ValueCountFrequency (%)
113914
69.0%
25174
 
25.7%
3733
 
3.6%
466
 
0.3%
561
 
0.3%
673
 
0.4%
7139
 
0.7%
ValueCountFrequency (%)
7139
 
0.7%
673
 
0.4%
561
 
0.3%
466
 
0.3%
3733
 
3.6%
25174
 
25.7%
113914
69.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
1
17450 
2
2710 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
117450
86.6%
22710
 
13.4%

Length

2022-04-13T10:07:52.761730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:52.847809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
117450
86.6%
22710
 
13.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Count of Walk Trips
Real number (ℝ≥0)

ZEROS

Distinct62
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.290823413
Minimum0
Maximum89
Zeros4878
Zeros (%)24.2%
Negative0
Negative (%)0.0%
Memory size157.6 KiB
2022-04-13T10:07:52.954970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile20
Maximum89
Range89
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.527906322
Coefficient of variation (CV)1.422823204
Kurtosis22.79438588
Mean5.290823413
Median Absolute Deviation (MAD)3
Skewness3.858543042
Sum106663
Variance56.6693736
MonotonicityNot monotonic
2022-04-13T10:07:53.093475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04878
24.2%
72485
12.3%
32185
10.8%
22095
10.4%
52034
10.1%
41456
 
7.2%
11297
 
6.4%
10591
 
2.9%
6587
 
2.9%
14539
 
2.7%
Other values (52)2013
10.0%
ValueCountFrequency (%)
04878
24.2%
11297
 
6.4%
22095
10.4%
32185
10.8%
41456
 
7.2%
52034
10.1%
6587
 
2.9%
72485
12.3%
8136
 
0.7%
998
 
0.5%
ValueCountFrequency (%)
891
 
< 0.1%
881
 
< 0.1%
852
 
< 0.1%
831
 
< 0.1%
806
 
< 0.1%
753
 
< 0.1%
721
 
< 0.1%
7026
0.1%
671
 
< 0.1%
651
 
< 0.1%

Count of Bike Trips
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2164186508
Minimum0
Maximum70
Zeros18475
Zeros (%)91.6%
Negative0
Negative (%)0.0%
Memory size157.6 KiB
2022-04-13T10:07:53.235685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum70
Range70
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.103834943
Coefficient of variation (CV)5.100461254
Kurtosis867.2725498
Mean0.2164186508
Median Absolute Deviation (MAD)0
Skewness18.78291469
Sum4363
Variance1.218451581
MonotonicityNot monotonic
2022-04-13T10:07:53.367781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
018475
91.6%
1710
 
3.5%
2406
 
2.0%
3229
 
1.1%
4110
 
0.5%
594
 
0.5%
741
 
0.2%
639
 
0.2%
1020
 
0.1%
810
 
< 0.1%
Other values (9)26
 
0.1%
ValueCountFrequency (%)
018475
91.6%
1710
 
3.5%
2406
 
2.0%
3229
 
1.1%
4110
 
0.5%
594
 
0.5%
639
 
0.2%
741
 
0.2%
810
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
701
 
< 0.1%
251
 
< 0.1%
211
 
< 0.1%
206
 
< 0.1%
152
 
< 0.1%
144
 
< 0.1%
126
 
< 0.1%
112
 
< 0.1%
1020
0.1%
93
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
2
12467 
3
5778 
1
1915 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
212467
61.8%
35778
28.7%
11915
 
9.5%

Length

2022-04-13T10:07:53.493759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:53.573104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
212467
61.8%
35778
28.7%
11915
 
9.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Count of adult household members
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.002331349
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.6 KiB
2022-04-13T10:07:53.635621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7465345779
Coefficient of variation (CV)0.3728326874
Kurtosis3.020708902
Mean2.002331349
Median Absolute Deviation (MAD)0
Skewness1.14094486
Sum40367
Variance0.557313876
MonotonicityNot monotonic
2022-04-13T10:07:53.735164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
212684
62.9%
14263
 
21.1%
32317
 
11.5%
4726
 
3.6%
5141
 
0.7%
627
 
0.1%
72
 
< 0.1%
ValueCountFrequency (%)
14263
 
21.1%
212684
62.9%
32317
 
11.5%
4726
 
3.6%
5141
 
0.7%
627
 
0.1%
72
 
< 0.1%
ValueCountFrequency (%)
72
 
< 0.1%
627
 
0.1%
5141
 
0.7%
4726
 
3.6%
32317
 
11.5%
212684
62.9%
14263
 
21.1%

Public Transportation or Taxi
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
4
9659 
2
4435 
3
3882 
1
2184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row4
4th row1
5th row2

Common Values

ValueCountFrequency (%)
49659
47.9%
24435
22.0%
33882
19.3%
12184
 
10.8%

Length

2022-04-13T10:07:53.849047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:53.922246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
49659
47.9%
24435
22.0%
33882
19.3%
12184
 
10.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
1
7424 
4
6062 
3
5399 
2
1275 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
17424
36.8%
46062
30.1%
35399
26.8%
21275
 
6.3%

Length

2022-04-13T10:07:54.015754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:54.091008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
17424
36.8%
46062
30.1%
35399
26.8%
21275
 
6.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Bicycle or Walk
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
4
13626 
3
2952 
2
2845 
1
 
737

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
413626
67.6%
32952
 
14.6%
22845
 
14.1%
1737
 
3.7%

Length

2022-04-13T10:07:54.183804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:54.265583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
413626
67.6%
32952
 
14.6%
22845
 
14.1%
1737
 
3.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Race White
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
1
17423 
0
2737 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
117423
86.4%
02737
 
13.6%

Length

2022-04-13T10:07:54.365833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:54.442292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
117423
86.4%
02737
 
13.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Race African
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
0
19086 
1
 
1074

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019086
94.7%
11074
 
5.3%

Length

2022-04-13T10:07:54.521818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:54.590451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
019086
94.7%
11074
 
5.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Race Asian
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
0
19128 
1
 
1032

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019128
94.9%
11032
 
5.1%

Length

2022-04-13T10:07:54.674608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:54.749080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
019128
94.9%
11032
 
5.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
0
20067 
1
 
93

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
020067
99.5%
193
 
0.5%

Length

2022-04-13T10:07:54.831309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:54.905366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
020067
99.5%
193
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
0
20114 
1
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
020114
99.8%
146
 
0.2%

Length

2022-04-13T10:07:54.977377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:55.038091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
020114
99.8%
146
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Multiple Response Race
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.6 KiB
0
19668 
1
 
492

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019668
97.6%
1492
 
2.4%

Length

2022-04-13T10:07:55.098826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T10:07:55.169622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
019668
97.6%
1492
 
2.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-13T10:07:43.841528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:22.664709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:24.467632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:26.529260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:28.344058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:30.242021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:32.499552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:34.338031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:36.479612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:38.151267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:39.785334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:41.681715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:44.023659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:22.850929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:24.607687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:26.678817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:28.486170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:30.394909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:32.656754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:34.504635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:36.645536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:38.270640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:39.924727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:41.868486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:44.263701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:22.989982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:24.757824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:26.827608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:28.665793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:30.550137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:32.799266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:34.680279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:36.795546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:38.399147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:40.059208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:42.063113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:44.463566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:23.132146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:24.888600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:26.978524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:28.819123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:30.728863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:32.939669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:34.864280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:36.940273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:38.533000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:40.427232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:42.231068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:44.688292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:23.289289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:25.013424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:27.111437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:28.964249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:30.903046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:33.064480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:35.038147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:37.064044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:38.689103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:40.563082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:42.440838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:44.886653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:23.445957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:25.326164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:27.243868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:29.116073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:31.070012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:33.196443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:35.210557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:37.197346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:38.844214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:40.729620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:42.616208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:45.046561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:23.614155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:25.490104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:27.403868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:29.262517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:31.293418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:33.343097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:35.389838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:37.338214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:38.978273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:40.870085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:42.781641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:45.193818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:23.781288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:25.660039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:27.569485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:29.443479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:31.507545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:33.508050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:35.568689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:37.480839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:39.108572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:40.999976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:42.973702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:45.432248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:23.922414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:25.814547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:27.740038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:29.592834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:31.675048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:33.680081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:35.737723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:37.609380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:39.239563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:41.116939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:43.114781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:45.644630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:24.055275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:25.981813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:27.884038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:29.745627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:31.816515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:33.848222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:35.938153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:37.745768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:39.375153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:41.254557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:43.324086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:45.838464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:24.191911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:26.125304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:28.028100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:29.889008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:31.948992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:34.000250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:36.096987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:37.868532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:39.495270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:41.392727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:43.505565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:45.980413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:24.344432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:26.337520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:28.184030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:30.084223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:32.302720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:34.160563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:36.313316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:38.008795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:39.641887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:41.535568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T10:07:43.689532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-13T10:07:55.286549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-13T10:07:55.771279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-13T10:07:56.237242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-13T10:07:56.695850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-13T10:07:57.031443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-13T10:07:46.363488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-13T10:07:47.741000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

AFVHousehold incomeHome OwnershipCount of household membersHousehold in urban/rural areapopulation densityPrice of Gasoline Affects TravelTravel is a Financial BurdenAgeGenderEducational AttainmentMore than One JobJob CategoryCount of Car Share Program UsageTrip Time to Work in MinutesBest estimate of annual milesCount of household vehiclescount of children in householdRelationshipFull-Time or Part-Time WorkerCount of Walk TripsCount of Bike TripsLevel of Physical ActivityCount of adult household membersPublic Transportation or TaxiPassenger to Friend/Family Member or Rental CarBicycle or WalkRace WhiteRace AfricanRace AsianRace American IndianRace Native HawaiianMultiple Response Race
00101215538205240188909.45390420115022144100000
1111121255401114012016562.572297202110032343100000
201012213350142407540634.08247160117122434100000
30714245333032302018453.73860822112022114100000
40111315225615220206201.92132830127022214100000
50111213426505240158016.04227020212222414100000
60613162261152201010712.02307720120023334100000
7071113426715240209498.96033420115021214100000
803211313661321058083.86481810123021414100000
9041115336313220911828.58565610114121414100000

Last rows

AFVHousehold incomeHome OwnershipCount of household membersHousehold in urban/rural areapopulation densityPrice of Gasoline Affects TravelTravel is a Financial BurdenAgeGenderEducational AttainmentMore than One JobJob CategoryCount of Car Share Program UsageTrip Time to Work in MinutesBest estimate of annual milesCount of household vehiclescount of children in householdRelationshipFull-Time or Part-Time WorkerCount of Walk TripsCount of Bike TripsLevel of Physical ActivityCount of adult household membersPublic Transportation or TaxiPassenger to Friend/Family Member or Rental CarBicycle or WalkRace WhiteRace AfricanRace AsianRace American IndianRace Native HawaiianMultiple Response Race
20150051113343014210109319.96402830115021414100000
20151051113343014210109138.00000030115021414100000
2015211024162334032406026137.19966430110022234100000
20153091216443613220306750.35735220117022422100000
201540922162363142201019235.18887520210022114100000
20155072114556214240515109.85205010117021232100000
20156011131644601524055253.83940520115023444100000
2015707211545371412028338.07421110110021344100000
201580913225431052403511610.315190211114032244100000
201590913225429152402526259.38940921212022214100000

Duplicate rows

Most frequently occurring

AFVHousehold incomeHome OwnershipCount of household membersHousehold in urban/rural areapopulation densityPrice of Gasoline Affects TravelTravel is a Financial BurdenAgeGenderEducational AttainmentMore than One JobJob CategoryCount of Car Share Program UsageTrip Time to Work in MinutesBest estimate of annual milesCount of household vehiclescount of children in householdRelationshipFull-Time or Part-Time WorkerCount of Walk TripsCount of Bike TripsLevel of Physical ActivityCount of adult household membersPublic Transportation or TaxiPassenger to Friend/Family Member or Rental CarBicycle or WalkRace WhiteRace AfricanRace AsianRace American IndianRace Native HawaiianMultiple Response Race# duplicates
005111622540223020800.0301181214411000002
10612224366022102312000.0502200222141000002
2081221424903110350.09011150224121000002
3081221425315140350.0902150324121000002
4091221325602230300.0902100224441000002
509231334540424010200.0602110233331000002
60111412545404240900.01201100244341000002